Industrial equipment operation, maintenance and optimization method and system based on complex network model

ABSTRACT

The present invention discloses an industrial equipment operation, maintenance and optimization method and system based on a complex network model. The method includes the following steps: obtaining data of all sensors of industrial equipment, and calculating a Spearman correlation coefficient between data of every two of the sensors within the same time period; using each sensor as a node, and using the Spearman correlation coefficient as a weight of a network edge, to construct a fully connected weighted network; and obtaining, when an adjustment instruction for a target feature is received, a currently optimal parameter adjustment path of the target feature based on the fully connected weighted network. In the present invention, production equipment in reality is digitized to construct a complex network oriented to industrial big data. An optimal path for equipment parameter tuning may be found by using the network, thereby reducing dependence of an enterprise on a domain expert.

BACKGROUND Technical Field

The present invention relates to the field of industrial big datainformation mining technologies, and in particular, to an industrialequipment operation, maintenance and optimization method and systembased on a complex network model.

Related Art

The description in this section merely provides background informationrelated to the present disclosure and does not necessarily constitutethe prior art.

With the continuous development of process industry, the processindustry is developing towards product diversification, diversificationof production stages, and diversification of production batches. In thisproduction mode, the status of production equipment usually determinesproduction efficiency of each production stage, and essentiallydetermines production efficiency of the entire factory. At the sametime, with the continuous improvement of requirements on the productquality and the continuous expansion of the production scale, theproduction mode of products is continuously improved. Therefore, theproduction process becomes further complex. Production complexityusually means that factories may use production equipment of differentbrands at the same time, and this means that the factory needs domainexperts from a plurality of equipment manufacturers to separatelyassemble and optimize their equipment, and as a result, the developmentof enterprises is highly dependent on the domain expert.

The arrival of Industry 4.0 and Made in China 2050 makes industrialproduction increasingly intelligent. Modern industry increasingly relieson data, and a data volume in industrial production starts to enter thePB level, and this brings a qualitative change in industrial datacompared with previous production data. A conventional data miningmanner is no longer suitable for analysis and processing of big data.

The development of the industrial big data technology makes theproduction process of the enterprises more intelligent, and themanufacturing industry is gradually changing from process-driven todata-driven. The industrial big data usually has respective features interms of a numerical value and a fluctuation range, and there is a largedifference in terms of a data size and a fluctuation status. Inaddition, the industrial big data technology has the problems of datamissing and large noise, which limits the wide application of industrialdata. Also, existing data-driven models of enterprises are usually quitelimited in the service aspect, and technical weaknesses beyond serviceexperience cannot be found. An existing data-based industrial networkmodel usually uses a conventional data mining algorithm which requires acomplex construction process, and the constructed data model is notverified.

SUMMARY

To overcome the foregoing disadvantages in the prior art, the presentinvention provides an industrial equipment operation, maintenance andoptimization method and system based on a complex network model.Production equipment in reality is digitized to construct a complexnetwork oriented to industrial big data. An optimal path for equipmentparameter tuning may be found by using the network, thereby reducingdependence of an enterprise on a domain expert.

To achieve the foregoing objective, one or more embodiments of thepresent invention provide the following technical solutions:

An industrial equipment operation, maintenance and optimization methodbased on a complex network model includes the following steps:

obtaining data of all sensors of industrial equipment, and calculating aSpearman correlation coefficient between data of every two of thesensors within the same time period;

using each sensor as a node, and using the Spearman correlationcoefficient as a weight of a network edge, to construct a fullyconnected weighted network; and

obtaining, when an adjustment instruction for a target feature isreceived, a currently optimal parameter adjustment path of the targetfeature based on the fully connected weighted network.

One or more embodiments provide an industrial equipment parameteradjustment path generation system based on a sensor network model,including:

a data obtaining module, configured to obtain data of all sensors ofindustrial equipment;

a network construction module, configured to calculate a Spearmancorrelation coefficient between data of every two of the sensors withinthe same time period; use each sensor as a node, and use the Spearmancorrelation coefficient as a weight of a network edge, to construct afully connected weighted network; and

a parameter adjustment path generation module, configured to obtain,when an adjustment instruction for a target feature is received, acurrently optimal parameter adjustment path of the target feature basedon the fully connected weighted network.

One or more embodiments provide an electronic device, including amemory, a processor, and a computer program stored in the memory andcapable of running on the processor, when executing the program, theprocessor implementing the industrial equipment operation, maintenanceand optimization method based on a complex network model.

One or more embodiments provide a computer-readable storage medium,storing a computer program, when executed by a processor, the programimplementing the industrial equipment operation, maintenance andoptimization method based on a complex network model.

The foregoing one or more technical solutions have the followingbeneficial effects:

In the present invention, production equipment in reality is digitizedby using industrial big data, to construct a complex network oriented tothe industrial big data, and various factors of the equipment areconnected. By using the network, an optimal path for equipment parametertuning may be found by traversing paths on the network, thereby reducingdependence of an enterprise on a domain expert.

In the model constructed in the present invention, a correlation betweendata is calculated by using a Spearman correlation coefficient. In thisway, an overall distribution of data and a sample size can be wellignored, to resolve problems of the industrial big data in theseaspects. In addition, the model is completely data-driven, so that animpact of service knowledge on the data model is greatly eliminated.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings constituting a part of the present inventionare used to provide further understanding of the present invention.Exemplary embodiments of the present invention and descriptions thereofare used to explain the present invention, and do not constitute animproper limitation to the present invention.

FIG. 1 is a flowchart of an industrial equipment operation, maintenanceand optimization method based on a complex network model according toone or more embodiments of the present invention;

FIG. 2 is a schematic diagram of Spearman correlation coefficientsbetween different sensors in a boiler according to one or moreembodiments of the present invention;

FIG. 3 is a schematic diagram of a complex network model of a boileraccording to one or more embodiments of the present invention; and

FIG. 4 is a flowchart of wavelet analysis according to one or moreembodiments of the present invention.

DETAILED DESCRIPTION

It should be noted that, the following detailed descriptions are allexemplary, and are intended to provide further descriptions of thepresent invention. Unless otherwise specified, all technical andscientific terms used herein have the same meanings as those usuallyunderstood by a person of ordinary skill in the art to which the presentinvention belongs.

It should be noted that the terms used herein are merely used fordescribing specific implementations, and are not intended to limitexemplary implementations of the present invention. For example, unlessotherwise specified in the context, singular forms are also intended toinclude plural forms. In addition, it should be further understood that,when the terms “comprise” and/or “include” are used in thisspecification, it indicates that there is a feature, a step, anoperation, a device, a component, and/or a combination thereof.

The embodiments in the present invention and features in the embodimentsmay be mutually combined in case that no conflict occurs.

Interpretation of Terms:

A complex network is a network presenting high complexity and thecomplexity is mainly represented in the following aspects: (1) a complexstructure, represented by a large quantity of nodes, and that thenetwork presents a plurality of different features; (2) networkevolution, represented by generation and disappearance of nodes orconnections; (3) connection diversity: there are differences inconnection weights between nodes, and the connection may be directional;(4) dynamic complexity: a node set may belong to the nonlinear dynamicsystem, for example, a status of the node changes complexly with time;(5) node diversity: nodes in a complex network may represent anything;and (6) multi-complexity integration, that is, the foregoingmulti-complexities affect each other.

Wavelet analysis is a multi-resolution analysis method in which anadaptive operation can be simultaneously performed in time domain andfrequency domain. During wavelet analysis, a signal (function) isgradually divided by using a scaling and translation operation, tofinally achieve time division at a high frequency and frequency divisionat a low frequency, and automatically adapt to a requirement oftime-frequency signal analysis, to focus on any detail of the signal.

The Spearman correlation coefficient is also referred to as the Spearmanrank correlation coefficient. “Rank” may be understood as a sequence orsorting order, which indicates solution according to a sorting positionof raw data. This type of representation form has no limitations asthose when a Pearson correlation coefficient is calculated.

Mathematically, the Spearman correlation coefficient is a coefficientthat measures an individual correlation between two columns ofvariables, and is irrelevant to a specific value of the variable and isonly related to a relative relationship (size sorting) between thevariables. The Spearman correlation coefficient is used herein as aweight of a node edge in a network model.

A support vector machine is a binary classification model of which apurpose is to find a hyperplane to segment samples. A principle ofsegmentation is interval maximization, which is finally transformed intoa convex quadratic programming problem to solve. When a training sampleis linearly separable, a linearly separable support vector machine islearned through hard interval maximization; when the training sample isapproximately linearly separable, a linear support vector machine islearned through soft interval maximization; when the training sample islinearly inseparable, a nonlinear support vector machine is learnedthrough core techniques and soft interval maximization.

Embodiment 1

This embodiment discloses an industrial equipment operation, maintenanceand optimization method based on a complex network model. As shown inFIG. 1 , the method includes the following steps:

Step 1: Obtain data of all sensors of industrial equipment, andcalculate a Spearman correlation coefficient between data of every twoof the sensors.

Different sensors on the same equipment are used as nodes of a network,and a Spearman correlation coefficient between data of the sensorswithin the same time period is used as a weight of a network edge, toconstruct a fully connected weighted complex network oriented to data.Descriptions are provided by using a boiler in a thermal powergeneration scenario as an example. In the thermal power generationproduction scenario, fuel heats water to generate steam when the fuel isburned. The steam pressure drives a turbine to rotate, and then theturbine drives a generator to rotate to generate electricity. In thisseries of energy conversion, the core that affects the efficiency ofpower generation is the combustion efficiency of the boiler, that is,fuel is burned to heat water, to generate high temperature and highpressure steam. There are many factors affecting the combustionefficiency of the boiler, including adjustable parameters of the boiler,such as a combustion feed rate, primary and secondary air, induced air,return air, and water supply; and working conditions of the boiler, suchas boiler temperature and pressure, furnace temperature and pressure,and a superheater temperature. Relevant data of the foregoinginfluencing factors may be acquired by using corresponding sensors.

Step 1 specifically includes:

Step 1.1: Select a network node, specifically, select all sensors of theboiler as the network nodes.

Step 1.2: Select a same time period t, and summarize data collected bythe sensors, a first column of data being data collected by a firstsensor V0, a second column of data being data collected by a secondsensor V1, and so on. A data set V=[V₀, V₁ . . . V_(n)] of workingstates of all the sensors of the boiler may be obtained, where V_(i)represents a sensor name on the boiler.

Step 1.3: Process missing data, specifically, process a data sequencereturned from each sensor as a time sequence, and in these sequences, ifa value of a sequence at a moment is NULL (that is, the sensor isabnormal at the moment and does not capture data), delete data of allthe sequences at the moment regardless of whether other sequences haveacquired data at the moment. In this way, the missing data isinvalidated to facilitate subsequent mining of association rules.

Step 1.4: Process the noise, specifically, denoise each signal by usingone layer of db4 wavelet, so that most of the signal noise can befiltered out after wavelet transform.

The wavelet transform is performed by using the following formula, whereα is a scale, and τ is a translation amount:

${{WT}\left( {\alpha,\tau} \right)} = {\frac{1}{\sqrt{\alpha}}{\int_{- \infty}^{\infty}{{f(t)}*{\varphi\left( \frac{t - \tau}{\alpha} \right)}{dt}}}}$

Step 1.5: Process a data distribution difference, where the datadistribution difference is processed by using a Spearman correlationcoefficient this time, and analyze a correlation between the data.Correlation coefficients between the sensors are calculated by using theSpearman correlation coefficient according to the data set V constructedin step 1.2, and a calculation formula is as follows:

$\rho = {1 - \frac{6{\sum_{i = 1}^{N}d_{i}^{2}}}{N\left( {N^{2} - 1} \right)}}$

A correlation coefficient matrix A may be obtained through calculation.

A specific calculation process is provided below by using a correlationbetween the signal V₀ and the signal V₁ as an example:

(1) Data of the column V₀ and the column V₁ is sorted according to adata size, to obtain data sets V₀* and V₁*, where V₀*=[v₀ ⁰, v₁ ⁰, . . ., v_(n) ⁰], and then a new column x_(i)=[1, 2, 3, . . . , n] is createdto assign data of V₀* a level value. Similarly, V₁*=[v₀ ¹, v₁ ¹, . . . ,v_(n) ¹] and a level value sequence y_(i)=[1, 2, 3, . . . , n] of V₁*may be obtained.

(2) Further, d_(i) ² may be obtained through calculation:

d₁² = (x₁ − y₁)² d₂² = (x₂ − y₂)² … d_(n)² = (x_(n) − y_(n))²

(3) Finally, a correlation coefficient

$\rho_{0 - 1} = {1 - \frac{6\left( {d_{1}^{2} + d_{2}^{2} + \ldots_{.} + d_{n}^{2}} \right)}{n\left( {n^{2} - 1} \right)}}$

between the signal V₀ and the signal V₁ may be obtained throughcalculation.

By analogy, a correlation coefficient ρ between the other sensors may beseparately calculated.

Step 1.6: For each piece of sensor data, remove sensor data of which acorrelation with the sensor data is less than a specified threshold. Forexample, a feature (sensor) of which a correlation coefficient is lessthan 0.1 is removed.

Step 2: Use each sensor as a node, and use the Spearman correlationcoefficient between data of the sensors within the same time period as aweight of a network edge, to construct a fully connected weightednetwork, as shown in FIG. 2 .

Step 3: Perform appropriacy check on the correlation coefficient in thefully connected weighted complex network.

In this embodiment, appropriacy check is performed only on one or morefeatures, specifically including the following steps: receiving aselection of a user for a production target, obtaining a correlationcoefficient matrix between data of a sensor corresponding to theproduction target and data of another sensor, and checking appropriacyof the correlation coefficient based on a support vector regressionmodel.

Step 3 specifically includes:

Step 3.1: Receive a selection of a user for a production target.Specifically, data of a sensor is selected as a main production targetof the equipment according to actual service experience. Using theboiler as an example, a steam amount may be selected as the mainproduction target. Then a correlation coefficient matrix A between dataof a sensor corresponding to the production target and data of anothersensor is obtained.

Step 3.2: Perform absolute value processing on the correlationcoefficient matrix A to obtain a matrix B, and remove a feature (sensor)of which a correlation coefficient with the target is less than 0.1 byusing the matrix B.

Step 3.3: Construct a prediction model by using a support vectorregression algorithm according to remaining features in step 3.2, andpredict the target value selected in step 3.1, where a prediction stepis as follows:

After the correlation coefficient is obtained through calculation, thecorrelation between the sensors is checked by using a support vectormachine model.

Main steps of the check are as follows:

(1) Divide the selected data into two parts: a training set and a testset.

(2) Perform cross validation by using data of the training set, to trainthe support vector machine model.

(3) Predict a target value by using data of the test set by using thetrained model.

(4) Compare a predicted result with an actual result, and determine thequality of the predicted result by using a root mean square error.

A support vector regression (SVR) model in the support vector machine isselected to perform prediction, and a derivation formula thereof is asfollows:

For a general regression problem, a training sample D{(x₁, y₁), (x₂,y₂), . . . , (x_(n), y_(n))}, y_(i)∈R is given, and f(x) that isapproximate to y to the greatest extent is expected to be learned, whereω, b are to-be-determined parameters. In the model, a loss is zero onlywhen f(x) and y are completely the same. However, in the support vectorregression model, it is assumed that a maximum deviation of ϵ betweenf(x) and y can be tolerated, and a loss is calculated only when anabsolute value of the difference between f(x) and y is greater than ϵ.In this case, it is equivalent to that an interval band with a width of2ϵ is constructed by using f(x) as a center, and if the training samplefalls within the interval band, it is considered that the trainingsample is predicted correctly.

Therefore, the SVR problem may be formalized as:

$\begin{matrix}{{\min_{\omega,b}\frac{1}{2}{\omega }^{2}} + {C{\sum_{i = 1}^{m}{Ł_{\epsilon}\left( {{f\left( x_{i} \right)} - y_{i}} \right)}}}} & (3)\end{matrix}$

C is a regularization constant, and Ł_(ϵ) is an insensitive lossfunction of E and satisfies the following condition:

$\begin{matrix}{{Ł_{\epsilon}(z)} = \left\{ \begin{matrix}{0,} & {{{if}{❘z❘}} \leq \epsilon} \\{{{❘z❘} - \epsilon},} & {otherwise}\end{matrix} \right.} & (4)\end{matrix}$

Further, slack variables ϵ and {circumflex over (ϵ)} may be introduced,and (4) is rewritten into the following form:

$\begin{matrix}{{\min_{\omega,b,\varepsilon_{i},{\hat{\varepsilon}}_{i}}\frac{1}{2}{\omega }^{2}} + {C{\sum_{i = 1}^{m}\left( {\varepsilon_{i,} + {\hat{\varepsilon}}_{i}} \right)}}} & (5)\end{matrix}$ s.t.f(x_(i)) − y_(i) ≤ ϵ + ε_(i),y_(i) − f(x_(i)) ≤ ϵ + ε̂_(i), ε_(i,) ≥ 0, ε̂_(i) ≥ 0, i = 1, 2, …, m.

Then, a Lagrange multiplier is introduced, and a Lagrange function canbe obtained by using the Lagrange multiplier method:

$\begin{matrix}{{L\left( {\omega,B,\alpha,\hat{\alpha},\varepsilon,\hat{\varepsilon},µ,\hat{\mu}} \right)} =} & (6)\end{matrix}$${\frac{1}{2}{\omega }^{2}} + {C{\sum_{i = 1}^{m}\left( {\varepsilon_{i,} + {\hat{\varepsilon}}_{i}} \right)}} - {\sum_{i = 1}^{m}{\mu_{i}\varepsilon_{i}}} - {\sum_{i = 1}^{m}{{\hat{\mu}}_{i}{\hat{\varepsilon}}_{i}}} + {\sum_{i = 1}^{m}{\alpha_{i}\left( {{f\left( x_{i} \right)} - y_{i} - \epsilon - \varepsilon_{i}} \right)}} +$$\sum_{i = 1}^{m}{{\hat{\alpha}}_{i}\left( {y_{i} - {f\left( x_{i} \right)} - \epsilon - {\hat{\varepsilon}}_{i}} \right)}$

Further, a duality problem of SVR may be obtained:

$\begin{matrix}{{\max_{\alpha,\hat{\alpha}}{\sum_{i = 1}^{m}{y_{i}\left( {{\hat{\alpha}}_{i} - \alpha_{i}} \right)}}} - {\epsilon\left( {{\hat{\alpha}}_{i} + \alpha_{i}} \right)} - {\frac{1}{2}{\sum_{i = 1}^{m}{\sum_{j = 1}^{m}{\left( {{\hat{\alpha}}_{i} - \alpha_{i}} \right)\left( {{\hat{\alpha}}_{j} - \alpha_{j}} \right)x_{i}^{T}x_{j}}}}}} & (7)\end{matrix}$${{s.t.{\sum_{i = 1}^{m}\left( {{\hat{\alpha}}_{i} - \alpha_{i}} \right)}} = 0},$0 ≤ α_(i), α̂_(i) ≤ C.

When the foregoing condition meets KKT, it can be learned that α_(i) cantake a non-zero value when and only when f(x_(i))−y_(i)−ϵ−ε_(i)=0.Similarly, {circumflex over (α)}_(i) can take a non-zero value when andonly when y_(i)−f(x_(i))−ϵ−ε_(i)=0. In other words, only when the sample(x_(i), y_(i)) does not fall within the interval band of ϵ, thecorresponding α_(i) and {circumflex over (α)}_(i) can take non-zerovalues. In addition, the foregoing constraints cannot be true at thesame time. Therefore, at least one of α_(i) and {circumflex over(α)}_(i) is zero. Based on this, a resolvent of SVR may be obtained asfollows:

f(x)=Σ_(i=1) ^(m)({circumflex over (α)}_(i)−α_(i))x _(i) ^(T) x+b  (8)

b=y _(i)+ϵ−Σ_(i=1) ^(m)({circumflex over (α)}_(i)−α_(i))x _(i) ^(T)x  (9)

TABLE 1 Model prediction result Model Training set mse Test set mseLinearSVR 0.0961 0.1158

When the prediction result meets an expectation, it is considered thatthe Spearman correlation coefficient can well represent the correlationbetween the sensors of the boiler.

After the correlation check succeeds, sensors on the boiler equipmentare selected as nodes of a complex network, and the Spearman correlationcoefficient obtained through calculation is used as the weight of thenetwork node edge, to construct the fully connected weighted complexnetwork oriented to industrial big data.

Step 4: Obtain, when a parameter adjustment instruction for a target isreceived, a currently optimal parameter adjustment path of the targetbased on the fully connected weighted network.

The optimal parameter adjustment path includes features directly relatedto the production target and features indirectly related to theproduction target. This step is to perform association rule mining onthe monitoring factors of the equipment. When the user needs to adjustthe production target, a plurality of directly related features of whichcorrelations with the production target are greater than a specifiedthreshold are searched for based on the fully connected weightednetwork, and then a plurality of indirectly related features of whichcorrelations with the plurality of directly related features are greaterthan a specified threshold are searched for separately. The directlyrelated features, the indirectly related features, and the correlationcoefficient are visualized for the user's reference. The visualizationmay be implemented in any existing visualization methods such as a treeform and an undirected graph, and this is not limited herein. The usermay select a feature according to a visualization result, and adjust acorresponding parameter.

Embodiment 2

An objective of this embodiment is to provide an industrial equipmentparameter adjustment path generation system based on a sensor networkmodel.

To achieve the foregoing objective, the present invention uses thefollowing technical solution:

This embodiment provides an industrial equipment parameter adjustmentpath generation system based on a sensor network model, including:

a data obtaining module, configured to obtain data of all sensors ofindustrial equipment;

a network construction module, configured to calculate a Spearmancorrelation coefficient between data of every two of the sensors withinthe same time period; use each sensor as a node, and use the Spearmancorrelation coefficient as a weight of a network edge, to construct afully connected weighted network; and

a parameter adjustment path generation module, configured to obtain,when an adjustment instruction for a target feature is received, acurrently optimal parameter adjustment path of the target feature basedon the fully connected weighted network.

Embodiment 3

An objective of this embodiment is to provide an electronic device.

An electronic device includes a memory, a processor, and a computerprogram stored in the memory and capable of running on the processor,when executing the program, the processor implementing the followingsteps, including:

obtaining data of all sensors of industrial equipment, and calculating aSpearman correlation coefficient between data of every two of thesensors within the same time period;

using each sensor as a node, and using the Spearman correlationcoefficient as a weight of a network edge, to construct a fullyconnected weighted network; and

obtaining, when an adjustment instruction for a target feature isreceived, a currently optimal parameter adjustment path of the targetfeature based on the fully connected weighted network.

Embodiment 4

An objective of this embodiment is to provide a computer-readablestorage medium.

A computer-readable storage medium stores a computer program, whenexecuted by a processor, the program implementing the following steps:

obtaining data of all sensors of industrial equipment, and calculating aSpearman correlation coefficient between data of every two of thesensors within the same time period;

using each sensor as a node, and using the Spearman correlationcoefficient as a weight of a network edge, to construct a fullyconnected weighted network; and

obtaining, when an adjustment instruction for a target feature isreceived, a currently optimal parameter adjustment path of the targetfeature based on the fully connected weighted network.

The steps involved in the foregoing Embodiment 2, Embodiment 3, andEmbodiment 4 correspond to Embodiment 1. For a specific implementation,refer to related descriptions of Embodiment 1. The term“computer-readable storage medium” should be understood as a singlemedium or a plurality of media including one or more instruction sets,and should also be understood as including any medium. The any mediumcan store, encode, or carry an instruction set used for being executedby a processor, and cause the processor to perform any method in thepresent invention.

The foregoing one or more embodiments have the following technicaleffects:

In the present invention, production equipment in reality is digitizedby using industrial big data, to construct a complex network oriented tothe industrial big data, and various factors of the equipment areconnected. By using the network, an optimal path for equipment parametertuning may be found by traversing paths on the network, thereby reducingdependence of an enterprise on a domain expert.

In the model constructed in the present invention, a correlation betweendata is calculated by using a Spearman correlation coefficient. In thisway, an overall distribution of data and a sample size can be wellignored, to resolve problems of the industrial big data in theseaspects. In addition, the model is completely data-driven, so that animpact of service knowledge on the data model is greatly eliminated.

A person skilled in the art should understand that the modules or stepsin the present invention may be implemented by using a general-purposecomputer apparatus. Optionally, they may be implemented by using programcode executable by a computing apparatus, so that they may be stored ina storage apparatus and executed by the computing apparatus.Alternatively, the modules or steps are respectively manufactured intovarious integrated circuit modules, or a plurality of modules or stepsthereof are manufactured into a single integrated circuit module. Thepresent invention is not limited to any specific combination of hardwareand software.

The foregoing descriptions are merely preferred embodiments of thepresent invention, but are not intended to limit the present invention.A person skilled in the art may make various alterations and variationsto the present invention. Any modification, equivalent replacement, orimprovement made within the spirit and principle of the presentinvention shall fall within the protection scope of the presentinvention.

Although the foregoing describes specific implementations of the presentinvention with reference to the accompanying drawings, the protectionscope of the present invention is not limited. A person skilled in theart should understand that, based on the technical solutions of thepresent invention, various modifications or variations made by a personskilled in the art without creative efforts shall still fall within theprotection scope of the present invention.

1. An industrial equipment operation, maintenance and optimizationmethod based on a complex network model, comprising the following steps:obtaining data of all sensors of industrial equipment, and calculating aSpearman correlation coefficient between data of every two of thesensors within a same time period; using each sensor as a node, andusing the Spearman correlation coefficient as a weight of a networkedge, to construct a fully connected weighted network; and obtaining,when an adjustment instruction for a target feature is received, acurrently optimal parameter adjustment path of the target feature basedon the fully connected weighted network.
 2. The industrial equipmentoperation, maintenance and optimization method according to claim 1,wherein after the data of all the sensors of the industrial equipment isobtained, missing data processing and noise processing are furtherperformed.
 3. The industrial equipment operation, maintenance andoptimization method according to claim 1, wherein a method forcalculating a Spearman correlation coefficient between data V₀ and V₁ oftwo sensors is: sorting the data V₀ and V₁ according to a data size, toobtain data sets V₀* and V₁*, creating a new column x_(i)=[1, 2, 3, . .. , n] to assign data of V₀* a level value, and creating a new columny_(i)=[1, 2, 3, . . . , n] to assign data of V₁* a level value, whereinthe Spearman correlation coefficient between V₀ and V₁ is:${\rho_{0 - 1} = {1 - \frac{6\left( {d_{1}^{2} + d_{2}^{2} + \ldots + d_{n}^{2}} \right)}{n\left( {n^{2} - 1} \right)}}},{wherein}$d_(i)² = (x_(i) − y_(i))².
 4. The industrial equipment operation,maintenance and optimization method according to claim 1, wherein afterthe Spearman correlation coefficient between the data of every two ofthe sensors within the same time period is calculated, for each sensor,a sensor of which a correlation with the sensor is less than a specifiedthreshold is removed.
 5. The industrial equipment operation, maintenanceand optimization method according to claim 1, wherein after the fullyconnected weighted network is constructed, appropriacy check isperformed on the weight in the network, comprising the following steps:receiving a selection of a user for a production target, and obtaining acorrelation coefficient matrix between data of a sensor corresponding tothe production target and data of another sensor; removing a sensor ofwhich a correlation with the sensor corresponding to the productiontarget is less than a specified threshold; and checking appropriacy ofthe correlation coefficient based on a support vector regression modelaccording to data of remaining sensors.
 6. The industrial equipmentoperation, maintenance and optimization method according to claim 5,wherein the checking appropriacy of the correlation coefficient based ona support vector regression model comprises: dividing the data of theremaining sensors into two parts: a training set and a test set;training the support vector regression model based on the training setby using a value of the production target as an output and using thedata of another sensor as an input; predicting, based on the test set,the production target by using the support vector regression model; anddetermining, according to a predicted value and an actual value of theproduction target, whether a predicted result reaches expectation, andif yes, determining that the correlation coefficient is appropriate. 7.The industrial equipment operation, maintenance and optimization methodaccording to claim 1, wherein the obtaining a currently optimalparameter adjustment path of the target feature comprises: searching,based on the fully connected weighted network, for a directly relatedfeature and an indirectly related feature of which correlations with thetarget feature are greater than a specified threshold, and visualizingthe directly related feature, the indirectly related feature, and thecorrelation coefficient.
 8. An industrial equipment parameter adjustmentpath generation system based on a complex network model, comprising: adata obtaining module, configured to obtain data of all sensors ofindustrial equipment; a network construction module, configured tocalculate a Spearman correlation coefficient between data of every twoof the sensors within the same time period; use each sensor as a node,and use the Spearman correlation coefficient as a weight of a networkedge, to construct a fully connected weighted network; and a parameteradjustment path generation module, configured to obtain, when anadjustment instruction for a target feature is received, a currentlyoptimal parameter adjustment path of the target feature based on thefully connected weighted network.
 9. An electronic device, comprising amemory, a processor, and a computer program stored in the memory andcapable of running on the processor, when executing the program, theprocessor implementing the industrial equipment operation, maintenanceand optimization method based on a complex network model according toclaim
 1. 10. A computer-readable storage medium, storing a computerprogram, when executed by a processor, the program implementing theindustrial equipment operation, maintenance and optimization methodbased on a complex network model according to claim 1.